Please explain!

Question 0. Manual Perceptron Training Below you can see the first 5 data points of the data set, all labeled as 'setosa'. Let's set the numerical value for 'setosa' to 1. (i.e. y = 1) In [5]: x[0:5], y[0:5] Out[5]: (array([[5.1, 1.4], [4.9, 1.4), [4.7, 1.3], [4.6, 1.5], [5. , 1.4]]), array([-1, -1, -1, -1, -1])) Suppose the initial weights of the perceptron are w0=0.1,w1=-0.2, W2=0.1 In the following space (Double click this text), write the weights after processing data points 0,1,2, and show your calculations (with n = 0.1): Addendum: If it is the case that all points are classified correctly, just flip all labels to the other class, so that they are all classified incorrectly, and then do the updates. (I think y = 1 for all labels should do that). Question 0. Manual Perceptron Training Below you can see the first 5 data points of the data set, all labeled as 'setosa'. Let's set the numerical value for 'setosa' to 1. (i.e. y = 1) In [5]: x[0:5], y[0:5] Out[5]: (array([[5.1, 1.4], [4.9, 1.4), [4.7, 1.3], [4.6, 1.5], [5. , 1.4]]), array([-1, -1, -1, -1, -1])) Suppose the initial weights of the perceptron are w0=0.1,w1=-0.2, W2=0.1 In the following space (Double click this text), write the weights after processing data points 0,1,2, and show your calculations (with n = 0.1): Addendum: If it is the case that all points are classified correctly, just flip all labels to the other class, so that they are all classified incorrectly, and then do the updates. (I think y = 1 for all labels should do that)